The Accelerating Rise of Women+ Conductors on Broadway

While it took 46 years to reach the first 50 unique conductors, that number doubled in just the next 24 years. Today, Leadership roles account for over 40% of positions in the 2020s.

A two-panel visualization exploring the growth of women+ conductors on Broadway from 1954 to present. The analysis tracks both the accelerating cumulative count of unique conductors and the shifting distribution of role tiers—from Support-heavy in the 1980s-90s to Leadership-dominant in the 2020s.

Original

The original visualization comes from Women+ Conductors on Broadway

Original visualization

Makeover

Figure 1: A two-panel visualization showing the growth of women+ conductors on Broadway. The left panel displays a cumulative area chart from 1954 to present, showing slow early growth that accelerates sharply after 2000, reaching approximately 100 unique conductors by 2024, with an annotation noting the 50th conductor was reached in 2000. The right panel shows horizontal stacked bar charts comparing role distribution across four eras: Pre-1980 was split between Leadership (49%) and Support (46%); 1980-1999 was dominated by Support roles (84%); 2000-2019 showed more balance with Support (50%), Leadership (26%), and Supplemental (23%); and 2020+ shows the most diverse mix with Supplemental (41%), Support (34%), and Leadership (25%). Sample sizes range from 39 positions pre-1980 to 102 in 2000-2019.

Steps to Create this Graphic

1. Load Packages & Setup

```{r}
#| label: load
#| warning: false
#| message: false
#| results: "hide"

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
  if (!require("pacman")) install.packages("pacman")
  pacman::p_load(
    tidyverse, ggtext, showtext, scales, glue, patchwork, janitor, readxl
)
})

### |- figure size ----
camcorder::gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  = 12,
    height = 8,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))
```

2. Read in the Data

```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false
#|

### |- Current data (2025) ----
conductor_raw <- readxl::read_excel(
   here::here("data/MakeoverMonday/2026/Conductor Timeline Data_Contest.xlsx")) |>
  clean_names()
```

3. Examine the Data

```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(conductor_raw)
skimr::skim(conductor_raw) |> summary()
```

4. Tidy Data

```{r}
#| label: tidy
#| warning: false

# Define role tiers
role_tiers <- tribble(
  ~role,                         ~tier,          ~tier_order,
  "Music Supervisor",            "Leadership",   1,
  "Music Director",              "Leadership",   1,
  "Conductor",                   "Leadership",   1,
  "Co-Music Director",           "Leadership",   1,
  "Co-Music Supervisor",         "Leadership",   1,
  "Associate Music Director",    "Support",      2,
  "Associate Music Supervisor",  "Support",      2,
  "Associate Conductor",         "Support",      2,
  "Resident Music Supervisor",   "Support",      2,
  "Assistant Music Director",    "Support",      2,
  "Assistant Conductor",         "Support",      2,
  "Alternate Conductor",         "Supplemental", 3,
  "Substitute Conductor",        "Supplemental", 3,
  "Children's Music Director",   "Specialized",  4
)

conductor_clean <- conductor_raw |>
  mutate(
    year = year(opening_date),
    era = case_when(
      year < 1980 ~ "Pre-1980",
      year >= 1980 & year < 2000 ~ "1980-1999",
      year >= 2000 & year < 2020 ~ "2000-2019",
      year >= 2020 ~ "2020+"
    )
  ) |>
  left_join(role_tiers, by = "role") |>
  filter(!is.na(era), !is.na(tier))

# Panel 1 Data: Cumulative
cumulative_data <- conductor_clean |>
  distinct(person_id, year) |>
  group_by(person_id) |>
  summarize(first_year = min(year), .groups = "drop") |>
  count(first_year) |>
  arrange(first_year) |>
  mutate(cumulative = cumsum(n))

latest_val <- filter(cumulative_data, first_year == max(first_year))

m50 <- filter(cumulative_data, cumulative >= 50) |> slice(1)

# Panel 2 Data: Role Stats with Chronological Ordering
role_era_stats <- conductor_clean |>
  group_by(era) |>
  mutate(era_n = n()) |>
  ungroup() |>
  mutate(era_label = glue("**{era}**<br>(n={era_n})")) |>
  mutate(era_label = fct_reorder(era_label, year, .desc = TRUE)) |>
  count(era_label, tier) |>
  group_by(era_label) |>
  mutate(pct = n / sum(n)) |>
  ungroup() |>
  mutate(tier = factor(tier, levels = rev(c("Leadership", "Support", "Supplemental", "Specialized"))))
```

5. Visualization Parameters

```{r}
#| label: params
#| include: true
#| warning: false

### |-  plot aesthetics ----
# Get base colors with custom palette
colors <- get_theme_colors(
  palette = list(
    leadership = "#134050", 
    support = "#4A90A4",
    supplemental = "#7FB3C4",
    specialized = "#B8D4DE"
  )
)

tier_colors <- c(
  "Leadership" = colors$palette$leadership,
  "Support" = colors$palette$support,
  "Supplemental" = colors$palette$supplemental,
  "Specialized" = colors$palette$specialized
)

### |-  Main titles ----
title_text <- "The Accelerating Rise of Women+ Conductors on Broadway"

subtitle_text <- str_glue(
  "While it took 46 years to reach the first 50 unique conductors, that number doubled in just the next 24 years.<br>",
  "Today, **Leadership roles** account for over 40% of positions in the 2020s."
)

caption_text <- create_mm_caption(
  mm_year = 2026, mm_week = 03,
  source_text = str_glue(
    "Maestra Music"
  )
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    # # Text styling
    plot.title = element_text(
      size = rel(1.5), family = fonts$title, face = "bold",
      color = colors$title, lineheight = 1.1, hjust = 0,
      margin = margin(t = 5, b = 10)
    ),
    plot.subtitle = element_markdown(
      size = rel(0.9), family = fonts$subtitle, face = "italic",
      color = alpha(colors$subtitle, 0.9), lineheight = 1.1,
      margin = margin(t = 0, b = 20)
    ),

    # Legend formatting
    legend.position = "plot",
    legend.justification = "right",
    legend.margin = margin(l = 12, b = 5),
    legend.key.size = unit(0.8, "cm"),
    legend.box.margin = margin(b = 10),

    # Axis formatting
    axis.line.x = element_line(color = "#252525", linewidth = .1),
    axis.ticks.y = element_blank(),
    axis.ticks.x = element_line(color = "gray", linewidth = 0.5),
    axis.title.x = element_text(
      face = "bold", size = rel(0.85),
      margin = margin(t = 10), family = fonts$subtitle,
      color = "gray40"
    ),
    axis.title.y = element_text(
      face = "bold", size = rel(0.85),
      margin = margin(r = 10), family = fonts$subtitle,
      color = "gray40"
    ),
    axis.text.x = element_text(
      size = rel(0.85), family = fonts$subtitle,
      color = "gray40"
    ),
    axis.text.y = element_markdown(
      size = rel(0.85), family = fonts$subtitle,
      color = "gray40"
    ),

    # Grid lines
    panel.grid.minor = element_line(color = "#ecf0f1", linewidth = 0.2),
    panel.grid.major = element_line(color = "#ecf0f1", linewidth = 0.4),

    # Margin
    plot.margin = margin(20, 20, 20, 20)
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

```{r}
#| label: plot
#| warning: false

### |-  p1: cummulative growth ----
p1 <-
  ggplot(cumulative_data, aes(first_year, cumulative)) +
  # Geoms
  geom_area(fill = colors$palette$leadership, alpha = 0.1) +
  geom_line(color = colors$palette$leadership, linewidth = 1.2) +
  geom_point(data = m50, color = colors$palette$leadership, size = 2) +
  geom_point(data = latest_val, color = colors$palette$leadership, size = 3) +
  # Annotate
  annotate("richtext",
    x = 1990, y = 62, label = "50th reached<br>in **2000**",
    fill = NA, label.color = NA, family = fonts$text, size = 3.5
  ) +
  annotate("text",
    x = 2024, y = 108, label = "103 Unique\nConductors",
    fontface = "bold", color = colors$leadership, family = fonts$text, hjust = 1, lineheight = 0.9
  ) +
  # Scales
  scale_x_continuous(limits = c(1954, 2025), breaks = seq(1960, 2020, 20)) +
  scale_y_continuous(limits = c(0, 115), breaks = seq(0, 100, 25)) +
  # Labs
  labs(title = "Cumulative Growth", x = NULL, y = NULL) +
  # Theme
  theme(panel.grid.minor = element_blank(), panel.grid.major.x = element_blank())

### |-  p2: role mix ----
# label data
label_data <- role_era_stats |>
  arrange(era_label, desc(tier)) |>
  group_by(era_label) |>
  mutate(
    cumsum_pct = cumsum(pct),
    label_pos = cumsum_pct - pct / 2,
    # Color logic: Light bars get dark text
    label_col = if_else(tier %in% c("Specialized", "Supplemental"), colors$text, "white"),
    # Label logic: Direct legend for top bar, % only for others if > 7%
    display_label = case_when(
      str_detect(era_label, "2020") ~ glue("**{percent(pct, 1)}**<br>{tier}"),
      pct >= 0.08 ~ glue("**{percent(pct, 1)}**"),
      TRUE ~ ""
    )
  )

p2 <-
ggplot(role_era_stats, aes(era_label, pct, fill = tier)) +
  # Geoms
  geom_col(width = 0.7, color = "white", linewidth = 0.2) +
  geom_richtext(data = label_data,
                aes(y = label_pos, label = display_label, color = label_col),
                fill = NA, label.color = NA, family = fonts$text, size = 3, lineheight = 1) +
  # Scales
  scale_fill_manual(values = tier_colors) +
  scale_color_identity() +
  scale_y_continuous(labels = percent_format(), expand = c(0, 0)) +
  coord_flip() +
  # Labs
  labs(title = "Role Mix by Era", x = NULL, y = NULL) +
  # theme_minimal(base_family = fonts$text) +
  # Theme
  theme(
    legend.position = "none", 
    panel.grid = element_blank(),
    axis.text.y = element_markdown(lineheight = 1.1)
    )

### |-  combined plot ----
combined_plot <- p1 + p2 +
  plot_layout(widths = c(1, 1.15)) +
  plot_annotation(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    theme = theme(
      plot.title = element_text(
        size = rel(1.8),
        family = fonts$title,
        face = "bold",
        color = colors$title,
        lineheight = 1.15,
        margin = margin(t = 5, b = 10)
      ),
      plot.subtitle = element_markdown(
        size = rel(0.75),
        family = fonts$subtitle,
        color = alpha(colors$subtitle, 0.88),
        lineheight = 1.5,
        margin = margin(t = 5, b = 10)
      ),
      plot.caption = element_markdown(
        size = rel(0.55),
        family = fonts$subtitle,
        color = colors$caption,
        hjust = 0,
        lineheight = 1.4,
        margin = margin(t = 20, b = 5)
      ),
    )
  )
```

7. Save

```{r}
#| label: save
#| warning: false

### |-  plot image ----  
save_plot_patchwork(
  plot = combined_plot, 
  type = "makeovermonday", 
  year = current_year,
  week = current_week,
  width = 12, 
  height = 8
  )
```

8. Session Info

Expand for Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1      readxl_1.4.3    janitor_2.2.0   patchwork_1.3.0
 [5] glue_1.8.0      scales_1.3.0    showtext_0.9-7  showtextdb_3.0 
 [9] sysfonts_0.8.9  ggtext_0.1.2    lubridate_1.9.3 forcats_1.0.0  
[13] stringr_1.5.1   dplyr_1.1.4     purrr_1.0.2     readr_2.1.5    
[17] tidyr_1.3.1     tibble_3.2.1    ggplot2_3.5.1   tidyverse_2.0.0
[21] pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.49          htmlwidgets_1.6.4  tzdb_0.5.0        
 [5] yulab.utils_0.1.8  vctrs_0.6.5        tools_4.4.0        generics_0.1.3    
 [9] curl_6.0.0         gifski_1.32.0-1    fansi_1.0.6        pkgconfig_2.0.3   
[13] ggplotify_0.1.2    skimr_2.1.5        lifecycle_1.0.4    compiler_4.4.0    
[17] farver_2.1.2       munsell_0.5.1      repr_1.1.7         codetools_0.2-20  
[21] snakecase_0.11.1   htmltools_0.5.8.1  yaml_2.3.10        pillar_1.9.0      
[25] camcorder_0.1.0    magick_2.8.5       commonmark_1.9.2   tidyselect_1.2.1  
[29] digest_0.6.37      stringi_1.8.4      labeling_0.4.3     rsvg_2.6.1        
[33] rprojroot_2.0.4    fastmap_1.2.0      grid_4.4.0         colorspace_2.1-1  
[37] cli_3.6.4          magrittr_2.0.3     base64enc_0.1-3    utf8_1.2.4        
[41] withr_3.0.2        timechange_0.3.0   rmarkdown_2.29     cellranger_1.1.0  
[45] hms_1.1.3          evaluate_1.0.1     knitr_1.49         markdown_1.13     
[49] gridGraphics_0.5-1 rlang_1.1.6        gridtext_0.1.5     Rcpp_1.0.13-1     
[53] xml2_1.3.6         renv_1.0.3         svglite_2.1.3      rstudioapi_0.17.1 
[57] jsonlite_1.8.9     R6_2.5.1           fs_1.6.5           systemfonts_1.1.0 

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in mm_2026_03.qmd.

For the full repository, click here.

10. References

Expand for References

1. Data:

   • Makeover Monday 2026 Week 3: Women+ Conductors on Broadway

2. Article

   • Women+ Conductors on Broadway

11. Custom Functions Documentation

📦 Custom Helper Functions

This analysis uses custom functions from my personal module library for efficiency and consistency across projects.

Functions Used:

• fonts.R: setup_fonts(), get_font_families() - Font management with showtext
• social_icons.R: create_social_caption() - Generates formatted social media captions
• image_utils.R: save_plot() - Consistent plot saving with naming conventions
• base_theme.R: create_base_theme(), extend_weekly_theme(), get_theme_colors() - Custom ggplot2 themes

Why custom functions?
These utilities standardize theming, fonts, and output across all my data visualizations. The core analysis (data tidying and visualization logic) uses only standard tidyverse packages.

Source Code:
View all custom functions → GitHub: R/utils